Frailty measurement in research and clinical practice: An updated review
Bibliographic record
Abstract
Frailty is a highly prevalent geriatric condition, affecting between 12-24% of older adults globally. It remains a major cause of morbidity and mortality in older adults. Incorporating frailty measurement into clinical decision making can guide optimal patient care. This updated review presents an outline of current frailty definitions and measurement approaches in both research and clinical practice, including: Fried's frailty phenotype; Rockwood and Mitnitski's Frailty Index (FI) of cumulative deficits; Clinical Frailty Scale (CFS); Fatigue, Resistance, Ambulation, Illness and Loss of weight (FRAIL) scale; Edmonton Frail Scale (EFS); electronic Frailty Index (eFI); Hospital Frailty Risk Score (HFRS); Study of Osteoporotic Fractures (SOF) Index; Tilburg Frailty Indicator (TFI); Groningen Frailty Indictor (GFI); Multidimensional Prognostic Index (MPI); the Kihon Checklist (KCL); Geriatric 8 (G8) for oncology; the Essential Frailty Toolset (EFT) for cardiology; plus gait speed and grip strength. The main strengths and limitations of existing frailty measurements are summarised, including how well these measurements operationalise frailty in terms of their accuracy in identifying frailty, their basis on biological causative theory, and their ability to reliably predict patient outcomes and response to potential therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".